A multinomial generalized linear mixed model for clustered competing risks data

Henrique Aparecido Laureano (), Ricardo Rasmussen Petterle, Guilherme Parreira da Silva, Paulo Justiniano Ribeiro Junior and Wagner Hugo Bonat
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Henrique Aparecido Laureano: Instituto de Pesquisa Pelé Pequeno Príncipe
Ricardo Rasmussen Petterle: Universidade Federal do Paraná
Guilherme Parreira da Silva: Universidade Federal do Paraná
Paulo Justiniano Ribeiro Junior: Universidade Federal do Paraná
Wagner Hugo Bonat: Universidade Federal do Paraná

Computational Statistics, 2024, vol. 39, issue 3, No 14, 1417-1434

Abstract: Abstract Clustered competing risks data are a complex failure time data scheme. Its main characteristics are the cluster structure, which implies a latent within-cluster dependence between its elements, and its multiple variables competing to be the one responsible for the occurrence of an event, the failure. To handle this kind of data, we propose a full likelihood approach, based on generalized linear mixed models instead the usual complex frailty model. We model the competing causes in the probability scale, in terms of the cumulative incidence function (CIF). A multinomial distribution is assumed for the competing causes and censorship, conditioned on the latent effects that are accommodated by a multivariate Gaussian distribution. The CIF is specified as the product of an instantaneous risk level function with a failure time trajectory level function. The estimation procedure is performed through the R package Template Model Builder, an C++ based framework with efficient Laplace approximation and automatic differentiation routines. A large simulation study was performed, based on different latent structure formulations. The model fitting was challenging and our results indicated that a latent structure where both risk and failure time trajectory levels are correlated is required to reach reasonable estimation.

Keywords: Cause-specific cumulative incidence function; Within-cluster dependence; Template model builder; Laplace approximation; Automatic differentiation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-023-01353-5

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